---
id: manipulation
title: "Manipulation & Grasping"
status: established
source_sections: "reference/sources/official-product-page.md, reference/sources/official-dex-hand.md, reference/sources/github-xr-teleoperate.md"
related_topics: [joint-configuration, sensors-perception, sdk-programming, learning-and-ai]
key_equations: []
key_terms: [dex3_1, inspire_hand, force_position_hybrid, tactile_sensor, teleoperation, xr_teleoperate]
images: []
examples: []
open_questions:
  - "Arm workspace envelope and reachability maps"
  - "Dex3-1 per-finger force limits"
  - "Dex3-1 maximum grasp payload (500g claimed — verify)"
  - "INSPIRE hand DOF and control details"
---

# Manipulation & Grasping

Arm control, hand dexterity, grasping strategies, and manipulation capabilities.

## 1. Arm Configuration

Arm DOF varies by G1 variant (see [[joint-configuration]]): [T0]

| Variant   | Joints per Arm | Wrist Articulation | Notes               |
|----------|---------------|--------------------|-----------------------|
| 23-DOF    | 5             | 1-axis (yaw only)  | Basic arm              |
| 29/43-DOF | 7             | 3-axis (yaw+pitch+roll) | Articulated wrist |

### Arm Payload

| Variant     | Payload per Arm | Source              | Tier |
|------------|----------------|----------------------|------|
| Standard    | 2 kg           | Official spec sheet  | T0   |
| EDU         | 3 kg           | Official spec sheet  | T0   |

## 2. End Effectors

Three hand options are available depending on variant: [T0]

### End Prosthetic Hand (Base Model)
- **Type:** Simplified gripper
- **DOF:** Minimal (open/close)
- **Use case:** Basic object handling
- **Available on:** G1 Standard

### Dex3-1 Three-Fingered Dexterous Hand (EDU A/B)
| Property              | Value                                |
|----------------------|--------------------------------------|
| Fingers               | 3 (thumb, index, middle)            |
| Total DOF per hand    | 7                                    |
| Thumb DOF             | 3 active                            |
| Index finger DOF      | 2 active                            |
| Middle finger DOF     | 2 active                            |
| Actuators             | 6 micro brushless direct-drive + 1 gear-drive |
| Tactile sensors       | 33 per hand                          |
| Control               | Force-position hybrid                |
| Grasp payload         | Up to 500g (reported) [T2]           |
| DDS command topic     | `rt/dex3/(left\|right)/cmd`          |
| DDS state topic       | `rt/dex3/(left\|right)/state`        |
| Message protocol      | `unitree_hg`                         |

**Capabilities:** Grasp bottles, tools, boxes; door handle manipulation; basic tool use; object sorting [T1]

### INSPIRE DFX Dexterous Hand (EDU C / Flagship Version C)
- **Type:** Full 5-finger advanced dexterous hand
- **DOF:** Higher than Dex3-1 (exact count per finger not yet confirmed)
- **Features:** Enhanced precision manipulation
- **Compatibility:** ROS2 teleoperation systems, multi-hand configurations
- **Documentation:** https://support.unitree.com/home/en/G1_developer/inspire_dfx_dexterous_hand [T0]

## 3. Grasping & Object Manipulation

Demonstrated capabilities with Dex3-1 hand: [T1 — Videos and demos]

- **Common objects:** Bottles, cups, tools, small boxes
- **Door manipulation:** Handle turning and door opening
- **Tool use:** Basic tool grasping and manipulation
- **Object sorting:** Pick-and-place operations

Grasping is typically performed using:
1. Visual servoing via D435i depth camera
2. Force-controlled approach using tactile feedback
3. Compliant grasping via force-position hybrid control

## 4. Whole-Body Manipulation (Loco-Manipulation)

Coordinating locomotion with arm manipulation is an active research area: [T1 — Research papers]

- **GR00T-WBC** (NVIDIA): Purpose-built whole-body control framework for G1 that separates locomotion (balance) from upper-body (manipulation) control. The recommended path for loco-manipulation. See [[whole-body-control]] for full details.
- **SoFTA framework** (arXiv: SoFTA paper): Slow-Fast Two-Agent RL decoupling upper and lower body with different execution frequencies for precise manipulation during locomotion
- **Safe control** (arXiv:2502.02858): Projected Safe Set Algorithm enforces limb-level geometric constraints for collision prevention during manipulation in cluttered environments

The G1-D platform (wheeled variant) is specifically designed for manipulation tasks, with a mobile base providing stable platform support. [T0]

### Mocap-Based Manipulation
Motion capture data can drive arm manipulation trajectories through the retargeting pipeline (see [[motion-retargeting]]). When combined with a whole-body controller (see [[whole-body-control]]), the robot can track human arm motions while maintaining balance — enabling demonstration-driven manipulation. [T1]

## 5. Teleoperation for Data Collection

Multiple teleoperation systems enable human demonstration collection: [T0 — GitHub repos]

### XR Teleoperation (xr_teleoperate)
| Property           | Value                                   |
|-------------------|------------------------------------------|
| Repository         | https://github.com/unitreerobotics/xr_teleoperate |
| Supported devices  | Apple Vision Pro, PICO 4 Ultra, Meta Quest 3 |
| Control modes      | Hand tracking, controller tracking       |
| Display modes      | Immersive, pass-through                  |
| Data recording     | Built-in episode capture                 |
| G1 configurations  | 29-DOF and 23-DOF                        |

Supported end-effectors: Dex1-1, Dex3-1, INSPIRE hand, BrainCo hands

### Kinect Teleoperation (kinect_teleoperate)
- Azure Kinect DK camera for body tracking
- MuJoCo 3.1.5 for visualization
- Safety "wake-up action" detection prevents accidental activation

## Key Relationships
- Uses: [[joint-configuration]] (arm + hand joints, DOF varies by variant)
- Uses: [[sensors-perception]] (D435i for visual servoing, tactile for contact)
- Controlled via: [[sdk-programming]] (DDS topics for hand control)
- May use: [[learning-and-ai]] (learned manipulation and loco-manipulation policies)
- Data collection: [[learning-and-ai]] (teleoperation → imitation learning)